Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations50000
Missing cells6510
Missing cells (%)0.8%
Total size in memory6.5 MiB
Average record size in memory136.0 B

Variable types

Numeric6
Text10

Alerts

post_code has 540 (1.1%) missing valuesMissing
post_code_prefix has 540 (1.1%) missing valuesMissing
country has 1201 (2.4%) missing valuesMissing
performance_date has 3432 (6.9%) missing valuesMissing
event_type has 797 (1.6%) missing valuesMissing
performance_reserved has 38880 (77.8%) zerosZeros

Reproduction

Analysis started2025-10-29 22:38:54.384498
Analysis finished2025-10-29 22:38:56.022961
Duration1.64 second
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct30684
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean943447.5238
Minimum6
Maximum1278647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-10-29T22:38:56.129959image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile99065.95
Q1680554.25
median1227247.5
Q31247852.75
95-th percentile1263850.05
Maximum1278647
Range1278641
Interquartile range (IQR)567298.5

Descriptive statistics

Standard deviation401708.1286
Coefficient of variation (CV)0.4257874641
Kurtosis-0.3403854641
Mean943447.5238
Median Absolute Deviation (MAD)39845.5
Skewness-1.034967901
Sum4.717237619 × 1010
Variance1.613694206 × 1011
MonotonicityNot monotonic
2025-10-29T22:38:56.303067image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9692547
 
0.1%
90450527
 
0.1%
123352323
 
< 0.1%
27483023
 
< 0.1%
77037123
 
< 0.1%
99802421
 
< 0.1%
156921
 
< 0.1%
46736020
 
< 0.1%
26518920
 
< 0.1%
9316418
 
< 0.1%
Other values (30674)49757
99.5%
ValueCountFrequency (%)
66
< 0.1%
282
 
< 0.1%
441
 
< 0.1%
481
 
< 0.1%
901
 
< 0.1%
ValueCountFrequency (%)
12786471
< 0.1%
12783271
< 0.1%
12687561
< 0.1%
12687541
< 0.1%
12687481
< 0.1%

post_code
Text

Missing 

Distinct25547
Distinct (%)51.7%
Missing540
Missing (%)1.1%
Memory size781.2 KiB
2025-10-29T22:38:56.841979image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length15
Median length7
Mean length7.264395471
Min length1

Characters and Unicode

Total characters359297
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15272 ?
Unique (%)30.9%

Sample

1st rowEH10 5DF
2nd rowWC2A 3QP
3rd rowE11 4RL
4th rowEH12 5QA
5th rowN7 0NX
ValueCountFrequency (%)
eh61188
 
1.2%
eh31157
 
1.2%
eh101075
 
1.1%
eh71034
 
1.1%
eh4946
 
1.0%
eh9855
 
0.9%
eh12807
 
0.8%
eh8790
 
0.8%
eh11780
 
0.8%
eh16569
 
0.6%
Other values (7428)87378
90.5%
2025-10-29T22:38:57.510539image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47120
 
13.1%
128453
 
7.9%
E23450
 
6.5%
H21458
 
6.0%
215988
 
4.4%
412965
 
3.6%
312924
 
3.6%
511645
 
3.2%
611376
 
3.2%
S11250
 
3.1%
Other values (63)162668
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)359297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47120
 
13.1%
128453
 
7.9%
E23450
 
6.5%
H21458
 
6.0%
215988
 
4.4%
412965
 
3.6%
312924
 
3.6%
511645
 
3.2%
611376
 
3.2%
S11250
 
3.1%
Other values (63)162668
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)359297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47120
 
13.1%
128453
 
7.9%
E23450
 
6.5%
H21458
 
6.0%
215988
 
4.4%
412965
 
3.6%
312924
 
3.6%
511645
 
3.2%
611376
 
3.2%
S11250
 
3.1%
Other values (63)162668
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)359297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47120
 
13.1%
128453
 
7.9%
E23450
 
6.5%
H21458
 
6.0%
215988
 
4.4%
412965
 
3.6%
312924
 
3.6%
511645
 
3.2%
611376
 
3.2%
S11250
 
3.1%
Other values (63)162668
45.3%

post_code_prefix
Text

Missing 

Distinct7427
Distinct (%)15.0%
Missing540
Missing (%)1.1%
Memory size781.2 KiB
2025-10-29T22:38:58.000625image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.357116862
Min length1

Characters and Unicode

Total characters264963
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2323 ?
Unique (%)4.7%

Sample

1st rowEH10 5
2nd rowWC2A 3
3rd rowE11 4
4th rowEH12 5
5th rowN7 0
ValueCountFrequency (%)
55622
 
5.8%
15426
 
5.6%
64969
 
5.1%
44934
 
5.1%
24834
 
5.0%
84818
 
5.0%
94753
 
4.9%
34591
 
4.8%
74110
 
4.3%
02918
 
3.0%
Other values (3696)49593
51.4%
2025-10-29T22:38:58.615243image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47110
17.8%
128426
 
10.7%
E17376
 
6.6%
215946
 
6.0%
H15420
 
5.8%
412934
 
4.9%
312891
 
4.9%
511613
 
4.4%
611335
 
4.3%
79329
 
3.5%
Other values (61)82583
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)264963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47110
17.8%
128426
 
10.7%
E17376
 
6.6%
215946
 
6.0%
H15420
 
5.8%
412934
 
4.9%
312891
 
4.9%
511613
 
4.4%
611335
 
4.3%
79329
 
3.5%
Other values (61)82583
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)264963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47110
17.8%
128426
 
10.7%
E17376
 
6.6%
215946
 
6.0%
H15420
 
5.8%
412934
 
4.9%
312891
 
4.9%
511613
 
4.4%
611335
 
4.3%
79329
 
3.5%
Other values (61)82583
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)264963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47110
17.8%
128426
 
10.7%
E17376
 
6.6%
215946
 
6.0%
H15420
 
5.8%
412934
 
4.9%
312891
 
4.9%
511613
 
4.4%
611335
 
4.3%
79329
 
3.5%
Other values (61)82583
31.2%

country
Text

Missing 

Distinct75
Distinct (%)0.2%
Missing1201
Missing (%)2.4%
Memory size781.2 KiB
2025-10-29T22:38:58.852354image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length32
Median length14
Mean length13.81108219
Min length5

Characters and Unicode

Total characters673967
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowUnited Kingdom
2nd rowUnited Kingdom
3rd rowUnited Kingdom
4th rowUnited Kingdom
5th rowUnited Kingdom
ValueCountFrequency (%)
united47568
49.3%
kingdom46235
47.9%
states1322
 
1.4%
ireland247
 
0.3%
germany147
 
0.2%
australia132
 
0.1%
canada93
 
0.1%
switzerland66
 
0.1%
france64
 
0.1%
taiwan39
 
< 0.1%
Other values (82)538
 
0.6%
2025-10-29T22:38:59.201119image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n94767
14.1%
d94348
14.0%
i94254
14.0%
t50538
7.5%
e49745
7.4%
47652
7.1%
U47572
7.1%
m46452
6.9%
o46387
6.9%
g46330
6.9%
Other values (39)55922
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)673967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n94767
14.1%
d94348
14.0%
i94254
14.0%
t50538
7.5%
e49745
7.4%
47652
7.1%
U47572
7.1%
m46452
6.9%
o46387
6.9%
g46330
6.9%
Other values (39)55922
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)673967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n94767
14.1%
d94348
14.0%
i94254
14.0%
t50538
7.5%
e49745
7.4%
47652
7.1%
U47572
7.1%
m46452
6.9%
o46387
6.9%
g46330
6.9%
Other values (39)55922
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)673967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n94767
14.1%
d94348
14.0%
i94254
14.0%
t50538
7.5%
e49745
7.4%
47652
7.1%
U47572
7.1%
m46452
6.9%
o46387
6.9%
g46330
6.9%
Other values (39)55922
8.3%
Distinct40819
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-10-29T22:38:59.530419image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters550000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35213 ?
Unique (%)70.4%

Sample

1st row180:3426755
2nd row180:3375038
3rd row180:3413971
4th row180:3361269
5th row180:3422897
ValueCountFrequency (%)
180:340640915
 
< 0.1%
180:338576315
 
< 0.1%
180:335935914
 
< 0.1%
180:338868114
 
< 0.1%
180:335182014
 
< 0.1%
180:337243913
 
< 0.1%
180:340993212
 
< 0.1%
180:336339912
 
< 0.1%
180:334871512
 
< 0.1%
180:336381311
 
< 0.1%
Other values (40809)49868
99.7%
2025-10-29T22:38:59.987037image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3103101
18.7%
874826
13.6%
074249
13.5%
173932
13.4%
:50000
9.1%
447406
8.6%
526101
 
4.7%
625966
 
4.7%
725564
 
4.6%
924576
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)550000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3103101
18.7%
874826
13.6%
074249
13.5%
173932
13.4%
:50000
9.1%
447406
8.6%
526101
 
4.7%
625966
 
4.7%
725564
 
4.6%
924576
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)550000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3103101
18.7%
874826
13.6%
074249
13.5%
173932
13.4%
:50000
9.1%
447406
8.6%
526101
 
4.7%
625966
 
4.7%
725564
 
4.6%
924576
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)550000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3103101
18.7%
874826
13.6%
074249
13.5%
173932
13.4%
:50000
9.1%
447406
8.6%
526101
 
4.7%
625966
 
4.7%
725564
 
4.6%
924576
 
4.5%

no_of_tickets
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.22006
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:00.115933image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum46
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.356668864
Coefficient of variation (CV)0.6110955849
Kurtosis50.70028691
Mean2.22006
Median Absolute Deviation (MAD)1
Skewness3.888957898
Sum111003
Variance1.840550407
MonotonicityNot monotonic
2025-10-29T22:39:00.228677image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
224898
49.8%
113483
27.0%
45042
 
10.1%
34245
 
8.5%
5973
 
1.9%
6751
 
1.5%
8219
 
0.4%
7183
 
0.4%
1067
 
0.1%
952
 
0.1%
Other values (13)87
 
0.2%
ValueCountFrequency (%)
113483
27.0%
224898
49.8%
34245
 
8.5%
45042
 
10.1%
5973
 
1.9%
ValueCountFrequency (%)
461
 
< 0.1%
381
 
< 0.1%
311
 
< 0.1%
206
< 0.1%
191
 
< 0.1%

performance_date
Text

Missing 

Distinct1658
Distinct (%)3.6%
Missing3432
Missing (%)6.9%
Memory size781.2 KiB
2025-10-29T22:39:00.619123image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters884792
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)0.3%

Sample

1st row2021-08-24 18:45:00
2nd row2021-08-12 14:30:00
3rd row2021-08-18 16:00:00
4th row2021-08-17 18:30:00
5th row2021-08-21 20:40:00
ValueCountFrequency (%)
2021-08-212730
 
2.9%
2021-08-282722
 
2.9%
2021-08-202585
 
2.8%
2021-08-142568
 
2.8%
2021-08-272463
 
2.6%
15:00:002408
 
2.6%
18:00:002308
 
2.5%
2021-08-132209
 
2.4%
18:30:002167
 
2.3%
2021-08-222146
 
2.3%
Other values (124)68830
73.9%
2025-10-29T22:39:01.115040image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0253142
28.6%
2138999
15.7%
1113641
12.8%
-93136
 
10.5%
:93136
 
10.5%
858867
 
6.7%
46568
 
5.3%
323119
 
2.6%
523016
 
2.6%
413287
 
1.5%
Other values (3)27881
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)884792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0253142
28.6%
2138999
15.7%
1113641
12.8%
-93136
 
10.5%
:93136
 
10.5%
858867
 
6.7%
46568
 
5.3%
323119
 
2.6%
523016
 
2.6%
413287
 
1.5%
Other values (3)27881
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)884792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0253142
28.6%
2138999
15.7%
1113641
12.8%
-93136
 
10.5%
:93136
 
10.5%
858867
 
6.7%
46568
 
5.3%
323119
 
2.6%
523016
 
2.6%
413287
 
1.5%
Other values (3)27881
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)884792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0253142
28.6%
2138999
15.7%
1113641
12.8%
-93136
 
10.5%
:93136
 
10.5%
858867
 
6.7%
46568
 
5.3%
323119
 
2.6%
523016
 
2.6%
413287
 
1.5%
Other values (3)27881
 
3.2%

event
Text

Distinct847
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:01.541692image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length106
Median length70
Mean length24.93536
Min length3

Characters and Unicode

Total characters1246768
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.1%

Sample

1st rowPolice Cops: Badass Be Thy Name
2nd rowThe Importance of Being... Earnest?
3rd rowEditburgh
4th rowSemi-Toned Presents: A Study in Burgundy
5th rowPatricia Gets Ready (for a date with the man that used to hit her)
ValueCountFrequency (%)
the14229
 
6.7%
of7280
 
3.4%
and3623
 
1.7%
3078
 
1.4%
a2943
 
1.4%
in2394
 
1.1%
story1710
 
0.8%
best1686
 
0.8%
1643
 
0.8%
show1605
 
0.8%
Other values (1921)173507
81.2%
2025-10-29T22:39:02.137559image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
168285
 
13.5%
e107998
 
8.7%
a77495
 
6.2%
o73052
 
5.9%
r62304
 
5.0%
n61152
 
4.9%
t55084
 
4.4%
i53415
 
4.3%
s53300
 
4.3%
h42371
 
3.4%
Other values (85)492312
39.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1246768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
168285
 
13.5%
e107998
 
8.7%
a77495
 
6.2%
o73052
 
5.9%
r62304
 
5.0%
n61152
 
4.9%
t55084
 
4.4%
i53415
 
4.3%
s53300
 
4.3%
h42371
 
3.4%
Other values (85)492312
39.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1246768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
168285
 
13.5%
e107998
 
8.7%
a77495
 
6.2%
o73052
 
5.9%
r62304
 
5.0%
n61152
 
4.9%
t55084
 
4.4%
i53415
 
4.3%
s53300
 
4.3%
h42371
 
3.4%
Other values (85)492312
39.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1246768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
168285
 
13.5%
e107998
 
8.7%
a77495
 
6.2%
o73052
 
5.9%
r62304
 
5.0%
n61152
 
4.9%
t55084
 
4.4%
i53415
 
4.3%
s53300
 
4.3%
h42371
 
3.4%
Other values (85)492312
39.5%

event_type
Text

Missing 

Distinct12
Distinct (%)< 0.1%
Missing797
Missing (%)1.6%
Memory size781.2 KiB
2025-10-29T22:39:02.317018image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length33
Median length19
Mean length8.969818913
Min length5

Characters and Unicode

Total characters441342
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComedy
2nd rowTheatre
3rd rowComedy
4th rowMusic
5th rowTheatre
ValueCountFrequency (%)
comedy20354
29.2%
theatre13781
19.7%
and6996
 
10.0%
music6564
 
9.4%
cabaret3187
 
4.6%
variety3187
 
4.6%
dance2257
 
3.2%
physical2257
 
3.2%
circus2257
 
3.2%
shows1843
 
2.6%
Other values (9)7138
 
10.2%
2025-10-29T22:39:02.601711image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e61518
13.9%
a38185
 
8.7%
d29462
 
6.7%
C27641
 
6.3%
r26076
 
5.9%
y25837
 
5.9%
o22852
 
5.2%
t21579
 
4.9%
20618
 
4.7%
m20393
 
4.6%
Other values (25)147181
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)441342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e61518
13.9%
a38185
 
8.7%
d29462
 
6.7%
C27641
 
6.3%
r26076
 
5.9%
y25837
 
5.9%
o22852
 
5.2%
t21579
 
4.9%
20618
 
4.7%
m20393
 
4.6%
Other values (25)147181
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)441342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e61518
13.9%
a38185
 
8.7%
d29462
 
6.7%
C27641
 
6.3%
r26076
 
5.9%
y25837
 
5.9%
o22852
 
5.2%
t21579
 
4.9%
20618
 
4.7%
m20393
 
4.6%
Other values (25)147181
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)441342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e61518
13.9%
a38185
 
8.7%
d29462
 
6.7%
C27641
 
6.3%
r26076
 
5.9%
y25837
 
5.9%
o22852
 
5.2%
t21579
 
4.9%
20618
 
4.7%
m20393
 
4.6%
Other values (25)147181
33.3%

performance_reserved
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2224
Minimum0
Maximum1
Zeros38880
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:02.697965image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4158625961
Coefficient of variation (CV)1.869885774
Kurtosis-0.2174906427
Mean0.2224
Median Absolute Deviation (MAD)0
Skewness1.335109755
Sum11120
Variance0.1729416988
MonotonicityNot monotonic
2025-10-29T22:39:02.786673image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
038880
77.8%
111120
 
22.2%
ValueCountFrequency (%)
038880
77.8%
111120
 
22.2%
ValueCountFrequency (%)
111120
 
22.2%
038880
77.8%

venue_server_id
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.8074
Minimum1
Maximum1835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:02.884793image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile61
Maximum1835
Range1834
Interquartile range (IQR)2

Descriptive statistics

Standard deviation175.1803982
Coefficient of variation (CV)5.507535925
Kurtosis79.86121845
Mean31.8074
Median Absolute Deviation (MAD)0
Skewness8.668346029
Sum1590370
Variance30688.17191
MonotonicityNot monotonic
2025-10-29T22:39:02.987926image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
132037
64.1%
36345
 
12.7%
333926
 
7.9%
142991
 
6.0%
611332
 
2.7%
1321221
 
2.4%
26894
 
1.8%
913473
 
0.9%
1835342
 
0.7%
34123
 
0.2%
Other values (3)316
 
0.6%
ValueCountFrequency (%)
132037
64.1%
36345
 
12.7%
142991
 
6.0%
22118
 
0.2%
26894
 
1.8%
ValueCountFrequency (%)
1835342
 
0.7%
913473
 
0.9%
231107
 
0.2%
1321221
2.4%
12491
 
0.2%

venue_id
Real number (ℝ)

Distinct108
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean374.51728
Minimum1
Maximum1225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:03.131085image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q119
median288
Q3732
95-th percentile1184
Maximum1225
Range1224
Interquartile range (IQR)713

Descriptive statistics

Standard deviation390.516361
Coefficient of variation (CV)1.042719207
Kurtosis-0.3920276488
Mean374.51728
Median Absolute Deviation (MAD)270
Skewness0.9275852276
Sum18725864
Variance152503.0282
MonotonicityNot monotonic
2025-10-29T22:39:03.287473image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14688
 
9.4%
2964576
 
9.2%
184543
 
9.1%
4063043
 
6.1%
262908
 
5.8%
2672881
 
5.8%
7342471
 
4.9%
3232402
 
4.8%
2772327
 
4.7%
8561772
 
3.5%
Other values (98)18389
36.8%
ValueCountFrequency (%)
14688
9.4%
220
 
< 0.1%
31145
 
2.3%
41439
 
2.9%
5461
 
0.9%
ValueCountFrequency (%)
12255
 
< 0.1%
122333
0.1%
12228
 
< 0.1%
12207
 
< 0.1%
121913
 
< 0.1%

venue
Text

Distinct115
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:03.691615image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length46
Median length39
Mean length24.18318
Min length5

Characters and Unicode

Total characters1209159
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAssembly George Square Gardens
2nd rowPleasance at EICC
3rd rowMeeting Point @ Ibis Hotel
4th rowtheSpace @ Symposium Hall
5th rowPleasance at EICC
ValueCountFrequency (%)
18665
 
10.2%
the10409
 
5.7%
thespace7657
 
4.2%
laughing7580
 
4.1%
horse7580
 
4.1%
hall7573
 
4.1%
george6128
 
3.3%
square6112
 
3.3%
assembly5897
 
3.2%
gardens4637
 
2.5%
Other values (219)101035
55.1%
2025-10-29T22:39:04.242258image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e142909
 
11.8%
133290
 
11.0%
a70017
 
5.8%
r64527
 
5.3%
o60436
 
5.0%
n59786
 
4.9%
s57087
 
4.7%
l54101
 
4.5%
i50270
 
4.2%
t47429
 
3.9%
Other values (57)469307
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1209159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e142909
 
11.8%
133290
 
11.0%
a70017
 
5.8%
r64527
 
5.3%
o60436
 
5.0%
n59786
 
4.9%
s57087
 
4.7%
l54101
 
4.5%
i50270
 
4.2%
t47429
 
3.9%
Other values (57)469307
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1209159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e142909
 
11.8%
133290
 
11.0%
a70017
 
5.8%
r64527
 
5.3%
o60436
 
5.0%
n59786
 
4.9%
s57087
 
4.7%
l54101
 
4.5%
i50270
 
4.2%
t47429
 
3.9%
Other values (57)469307
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1209159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e142909
 
11.8%
133290
 
11.0%
a70017
 
5.8%
r64527
 
5.3%
o60436
 
5.0%
n59786
 
4.9%
s57087
 
4.7%
l54101
 
4.5%
i50270
 
4.2%
t47429
 
3.9%
Other values (57)469307
38.8%

subvenue_id
Real number (ℝ)

Distinct151
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean808.26748
Minimum1
Maximum1858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:04.386189image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q176
median601
Q31782
95-th percentile1805
Maximum1858
Range1857
Interquartile range (IQR)1706

Descriptive statistics

Standard deviation732.6205182
Coefficient of variation (CV)0.9064085051
Kurtosis-1.645852334
Mean808.26748
Median Absolute Deviation (MAD)559
Skewness0.3061541322
Sum40413374
Variance536732.8238
MonotonicityNot monotonic
2025-10-29T22:39:04.540721image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
424334
 
8.7%
14763503
 
7.0%
17872881
 
5.8%
17892471
 
4.9%
3602327
 
4.7%
6012159
 
4.3%
17821529
 
3.1%
551512
 
3.0%
17841442
 
2.9%
761391
 
2.8%
Other values (141)26451
52.9%
ValueCountFrequency (%)
1385
0.8%
2560
1.1%
3624
1.2%
4539
1.1%
515
 
< 0.1%
ValueCountFrequency (%)
185811
 
< 0.1%
18572
 
< 0.1%
18555
 
< 0.1%
185333
0.1%
18522
 
< 0.1%
Distinct140
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:04.961520image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length55
Median length37
Mean length15.50304
Min length4

Characters and Unicode

Total characters775152
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowSpiegeltent Palais Du Variete
2nd rowLomond Theatre
3rd rowCoffee Bar in Lobby Area
4th rowGarden Theatre
5th rowLomond Theatre
ValueCountFrequency (%)
theatre12735
 
10.6%
the7806
 
6.5%
spiegeltent4334
 
3.6%
du4334
 
3.6%
variete4334
 
3.6%
palais4334
 
3.6%
garden3550
 
2.9%
bevan2881
 
2.4%
online2707
 
2.2%
main2489
 
2.1%
Other values (196)70978
58.9%
2025-10-29T22:39:05.539912image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e128781
16.6%
71523
 
9.2%
a70176
 
9.1%
r56304
 
7.3%
n44896
 
5.8%
t43683
 
5.6%
i34649
 
4.5%
l32863
 
4.2%
h31263
 
4.0%
o24317
 
3.1%
Other values (53)236697
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)775152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e128781
16.6%
71523
 
9.2%
a70176
 
9.1%
r56304
 
7.3%
n44896
 
5.8%
t43683
 
5.6%
i34649
 
4.5%
l32863
 
4.2%
h31263
 
4.0%
o24317
 
3.1%
Other values (53)236697
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)775152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e128781
16.6%
71523
 
9.2%
a70176
 
9.1%
r56304
 
7.3%
n44896
 
5.8%
t43683
 
5.6%
i34649
 
4.5%
l32863
 
4.2%
h31263
 
4.0%
o24317
 
3.1%
Other values (53)236697
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)775152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e128781
16.6%
71523
 
9.2%
a70176
 
9.1%
r56304
 
7.3%
n44896
 
5.8%
t43683
 
5.6%
i34649
 
4.5%
l32863
 
4.2%
h31263
 
4.0%
o24317
 
3.1%
Other values (53)236697
30.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-10-29T22:39:05.667041image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length26
Median length3
Mean length3.33946
Min length3

Characters and Unicode

Total characters166973
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb
ValueCountFrequency (%)
web48598
93.7%
phones1215
 
2.3%
customer629
 
1.2%
services629
 
1.2%
629
 
1.2%
default187
 
0.4%
2025-10-29T22:39:05.880922image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e51887
31.1%
W48598
29.1%
b48598
29.1%
s2473
 
1.5%
1887
 
1.1%
o1844
 
1.1%
r1258
 
0.8%
n1215
 
0.7%
h1215
 
0.7%
P1215
 
0.7%
Other values (13)6783
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)166973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e51887
31.1%
W48598
29.1%
b48598
29.1%
s2473
 
1.5%
1887
 
1.1%
o1844
 
1.1%
r1258
 
0.8%
n1215
 
0.7%
h1215
 
0.7%
P1215
 
0.7%
Other values (13)6783
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)166973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e51887
31.1%
W48598
29.1%
b48598
29.1%
s2473
 
1.5%
1887
 
1.1%
o1844
 
1.1%
r1258
 
0.8%
n1215
 
0.7%
h1215
 
0.7%
P1215
 
0.7%
Other values (13)6783
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)166973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e51887
31.1%
W48598
29.1%
b48598
29.1%
s2473
 
1.5%
1887
 
1.1%
o1844
 
1.1%
r1258
 
0.8%
n1215
 
0.7%
h1215
 
0.7%
P1215
 
0.7%
Other values (13)6783
 
4.1%